Tunnel blasting slab staggering detection method and device, computer equipment, medium and product

Abstract

The invention relates to the technical field of tunnel engineering, and discloses a tunnel blasting slab staggering detection method and device, computer equipment, a medium and a product.The tunnel blasting slab staggering detection method comprises the steps that after tunnel blasting excavation is conducted, tunnel point cloud is collected, and the point cloud over-excavation and under-excavation value of the tunnel point cloud is calculated; performing region division on the tunnel point cloud based on a preset division method, and calculating a region over-break and under-break average value of each point cloud region; performing curve fitting on the regional back break average value to obtain a back break fitting curve; and determining the slab staggering between the tunnel blasting cycles based on the wave crests and the wave troughs of the back break fitting curve. The method can improve the blasting slab staggering detection efficiency and accuracy.

Description

Technical Field

[0001] The present invention relates to the technical field of tunnel engineering, and in particular to a method, device, computer equipment, medium and product for detecting misalignment of tunnel blasting. Background Art

[0002] Tunnel engineering is a building built underground or underwater or in a mountain to lay railways or build roads for motor vehicles to pass. Tunnel blasting is a common excavation method in tunnels and underground projects. It uses explosives to break rocks or soil to achieve a predetermined excavation profile. When a tunnel is excavated by blasting, the surrounding blasting holes are usually tilted outward at a certain angle to ensure that the excavation diameter meets the requirements. Therefore, there will be a certain amount of misalignment between blasting cycles, resulting in an uneven inner wall of the tunnel, which affects the quality of subsequent construction processes.

[0003] Existing misalignment detection is mainly done manually by local sampling detection and measurement, such as carefully checking the surface of the excavated rock mass under natural light or artificial light, recording the misalignment position and its approximate size, or using tools such as rulers and feeler gauges to measure the specific value of the misalignment. The current method has low measurement efficiency and poor accuracy. Summary of the invention

[0004] In view of this, the present invention provides a method, device, computer equipment, medium and product for detecting misaligned blasting in a tunnel, so as to improve the efficiency and accuracy of detecting misaligned blasting.

[0005] In a first aspect, the present invention provides a method for detecting tunnel blasting misalignment, which comprises: after tunnel blasting excavation, collecting tunnel point clouds and calculating point cloud over- and under-excavation values ​​of the tunnel point clouds; dividing the tunnel point clouds into regions based on a preset division method, and calculating the regional over- and under-excavation average value of each point cloud region; performing curve fitting on the regional over- and under-excavation average values ​​to obtain an over- and under-excavation fitting curve; and determining the misalignment between tunnel blasting cycles based on the peaks and troughs of the over- and under-excavation fitting curve.

[0006] In this implementation, a three-dimensional point cloud detection method is used to collect tunnel point clouds, and the overall tunnel point cloud is divided. The average value of regional over-excavation and under-excavation is used to characterize the tunnel blasting conditions in the area, and the average value of regional over-excavation and under-excavation is representative of the region. Furthermore, a continuous over-excavation fitting curve is obtained by fitting, which can convert discrete point data into continuous data, and can characterize the tunnel blasting conditions of the overall continuous area, so as to achieve a comprehensive detection of the tunnel. The existing misalignment is determined based on the relationship between the peaks and troughs of the over-excavation and under-excavation fitting curves. The intelligent detection method has high detection efficiency and reliable detection results.

[0007] In an optional embodiment, the tunnel point cloud is divided into regions based on a preset division method, and the average regional over-excavation and under-excavation value of each point cloud region is calculated, including: determining tunnel strips at preset positions on the cross-sectional contour of the tunnel, sampling the tunnel point cloud, and obtaining corresponding point cloud strips; dividing the tunnel point cloud on the point cloud strips based on a preset division method, and calculating the average regional over-excavation and under-excavation value of each point cloud region.

[0008] In this implementation, strip point cloud sampling is performed according to preset locations, and the preset locations are determined according to actual detection requirements, so that the selected tunnel point cloud is representative and the amount of data and calculation is reduced. Regional division is performed based on the point cloud strips, and the average value of regional over-excavation and under-excavation can accurately characterize the blasting conditions of each area of ​​the tunnel strip.

[0009] In an optional embodiment, the tunnel point cloud on the point cloud strip is divided into regions based on a preset division method, and the regional over-excavation average value of each point cloud region is calculated, including: dividing the tunnel point cloud on the point cloud strip according to the interval range of the pile number to obtain multiple point cloud regions, each point cloud region includes multiple target pile number point clouds, and the pile numbers of adjacent point cloud regions are adjacent; obtaining the point cloud over-excavation value of the target pile number point cloud, and calculating the over-excavation average value of all target pile number point clouds in the point cloud region to obtain the corresponding regional over-excavation average value.

[0010] In this implementation, the point cloud strips are divided according to the pile numbers, which provides a division basis and can plan the point cloud strips according to the pile number direction, laying the foundation for the subsequent fitting of the over-break and under-break fitting curve. By calculating the average over-break and under-break values ​​of all target pile number point clouds in the point cloud area, the blasting conditions in the pile number area can be accurately characterized, thereby improving the efficiency of subsequent misalignment detection.

[0011] In an optional implementation, curve fitting is performed on the regional overbreak and underbreak average value to obtain the overbreak fitting curve, including: curve fitting is performed on the regional overbreak and underbreak average value of the point cloud area in order of pile numbers to obtain the overbreak fitting curve.

[0012] In this implementation, curve fitting is performed according to the pile number sequence, so that the over-break and under-break fitting curve obtained by fitting can accurately characterize the change of the tunnel surface along the pile number sequence.

[0013] In an optional embodiment, the misalignment between tunnel blasting cycles is determined based on the peaks and troughs of the over-underbreak fitting curve, including: determining local peaks and local troughs based on the peaks and troughs of the over-underbreak fitting curve; deleting the troughs within the local peaks and the peaks with the smallest average regional over-underbreak values, and deleting the peaks within the local troughs and the troughs with the largest average regional over-underbreak values ​​to obtain updated peaks and updated troughs; determining the misalignment between tunnel blasting cycles between adjacent updated peaks and updated troughs, and calculating the difference between the average regional over-underbreak values ​​of adjacent updated peaks and updated troughs to obtain the misalignment height.

[0014] In this implementation, peaks and troughs are used to determine the existing misalignment, while the impact of smaller peaks and troughs on misalignment detection is taken into account. Peaks and troughs with smaller continuous fluctuations are deleted, and peaks and troughs with larger distances and larger fluctuations are retained. This can effectively screen peaks and troughs and improve the accuracy of subsequent misalignment detection.

[0015] In an optional embodiment, local peaks and local troughs are determined based on the peaks and troughs of the over-and-underbreak fitting curve, including: obtaining the peak pile number difference between adjacent peaks, and when the peak pile number difference is less than a first pile number threshold, determining that the adjacent peaks are local peaks; obtaining the trough pile number difference between adjacent troughs, and when the trough pile number difference is less than a second pile number threshold, determining that the adjacent troughs are local troughs.

[0016] In this implementation, whether there are local peaks and local troughs is determined by the pile number distance. The detection method is simple and accurate, and the detection efficiency is improved.

[0017] In the second aspect, the present invention provides a tunnel blasting misalignment detection device, which includes: an acquisition module, used to acquire tunnel point clouds after tunnel blasting and excavation, and calculate the point cloud over-excavation and under-excavation values ​​of the tunnel point clouds; a division module, used to divide the tunnel point clouds into regions based on a preset division method, and calculate the regional over-excavation and under-excavation average value of each point cloud region; a fitting module, used to perform curve fitting on the regional over-excavation and under-excavation average value to obtain an over-excavation and under-excavation fitting curve; a determination module, used to determine the misalignment between tunnel blasting cycles based on the peaks and troughs of the over-excavation and under-excavation fitting curve.

[0018] In a third aspect, the present invention provides a computer device, comprising: a memory and a processor, the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the tunnel blasting misalignment detection method of the first aspect or any corresponding embodiment thereof by executing the computer instructions.

[0019] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the tunnel blasting misalignment detection method of the first aspect or any corresponding embodiment thereof.

[0020] In a fifth aspect, the present invention provides a computer program product, comprising computer instructions for causing a computer to execute the tunnel blasting misalignment detection method of the first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0022] Figure 1 is a schematic diagram of a tunnel blasting stagger according to an embodiment of the present invention; Figure 2 is a flow chart of a method for detecting misalignment of tunnel blasting according to an embodiment of the present invention; Figure 3 is a schematic diagram of a tunnel point cloud according to an embodiment of the present invention; Figure 4 is a schematic diagram of a tunnel section according to an embodiment of the present invention; Figure 5 is a flow chart of another method for detecting misalignment of tunnel blasting according to an embodiment of the present invention; Figure 6 is a schematic diagram of a tunnel strip according to an embodiment of the present invention; Figure 7 is a schematic diagram of an average value of regional over-excavation and under-excavation according to an embodiment of the present invention; Figure 8 is a schematic diagram of an over-under excavation fitting curve according to an embodiment of the present invention; Fig. 9 is a structural block diagram of a tunnel blasting misalignment detection device according to an embodiment of the present invention; Fig.10 It is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0023] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0024] When tunnels are excavated by blasting, the surrounding blasting holes are usually tilted outward at a certain angle to ensure that the excavation diameter meets the requirements. Therefore, there will be a certain misalignment between blasting cycles, resulting in uneven tunnel walls and affecting the quality of subsequent construction processes. Figure 1 , Figure 1 Schematic diagram of a tunnel blasting stagger according to an embodiment of the present invention. Figure 1 As shown in the figure, the dotted line represents the tunnel boundary, that is, the initial designed tunnel boundary line. However, due to the uncertainty in the actual blasting process, it is impossible to strictly excavate the expected tunnel according to the tunnel boundary. Generally, the tunnel obtained by actual excavation will be near the tunnel boundary, forming a tunnel like Figure 1 The tunnel shown. Specifically, during tunnel blasting and excavation, multiple blasting boreholes are formed from the outside to the inside along the tunnel excavation direction, and a blasting misalignment is formed between two blasting boreholes. Timely detection and evaluation of misalignment can be used to evaluate current blasting parameters and design more reasonable blasting parameters for subsequent blasting to control the misalignment between blasting cycles. Therefore, the present application proposes a tunnel blasting misalignment detection method, which uses an intelligent detection method to improve detection efficiency and improve the reliability of detection results.

[0025] According to an embodiment of the present invention, a method, apparatus, computer device, medium and product method embodiment of tunnel blasting misalignment detection are provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0026] In this embodiment, a method for detecting tunnel blasting misalignment is provided. Figure 2 is a flow chart of a method for detecting misalignment of tunnel blasting according to an embodiment of the present invention. It should be noted that if there are substantially the same results, this embodiment is not limited to Figure 2 The process sequence shown is limited. Figure 2 As shown, the process includes the following steps: Step S201, after the tunnel is excavated by blasting, the tunnel point cloud is collected, and the over-excavation and under-excavation values ​​of the tunnel point cloud are calculated.

[0027] After the tunnel is excavated by blasting, 3D laser scanning technology is used to collect tunnel point clouds from the tunnel and generate a 3D point cloud model of the actual tunnel. Figure 3 , Figure 3 is a schematic diagram of a tunnel point cloud according to an embodiment of the present invention, such as Figure 3 As shown, according to the tunnel blasting direction, the actual tunnel 3D point cloud model is generated based on the collected tunnel point cloud.

[0028] In one implementation, the present application further optimizes the generated actual tunnel 3D point cloud model. Specifically, the actual tunnel 3D point cloud model is converted to a unified engineering coordinate system, and all tunnel point clouds in the actual tunnel 3D point cloud model are subjected to noise reduction processing to remove invalid tunnel point clouds. For example, outlier noise tunnel point clouds are proposed by density-based spatial clustering method.

[0029] Furthermore, the actual three-dimensional point cloud model of the tunnel is compared with the designed tunnel model, and the point cloud over-excavation and under-excavation value of each tunnel point cloud in the actual three-dimensional point cloud model is calculated.

[0030] Among them, over-break and under-break refer to the difference between the actual excavation volume and the designed excavation volume in earthwork engineering. Over-break and under-break include over-break and under-break. Over-break refers to the part where the actual excavation volume exceeds the designed excavation volume, and under-break refers to the part where the actual excavation volume does not reach the designed excavation volume.

[0031] The point cloud over-excavation and under-excavation values ​​include the point cloud over-excavation value and the point cloud under-excavation value. The point cloud over-excavation value is the distance that the tunnel point cloud exceeds the designed tunnel section, and the point cloud under-excavation value is the distance that the tunnel point cloud does not reach the designed tunnel section.

[0032] See also Figure 4 , Figure 4 Schematic diagram of a tunnel section according to an embodiment of the present invention. Figure 4 As shown in the figure, the cross section of the designed tunnel model is a regular straight line segment in the ground area and a regular cross section arc segment in the non-ground area. However, the tunnel obtained by actual excavation has an irregular cross section, in which the difference between the cross section point cloud in the actual 3D point cloud model and the designed tunnel model is the point cloud over-excavation value.

[0033] In one implementation, the present application uses a cross-sectional projection method to calculate the over- and under-excavation values ​​of a point cloud.

[0034] Specifically, each tunnel point cloud in the actual tunnel 3D point cloud model is vertically projected onto the section where the initial design tunnel model is located, and the minimum distance between the projection point and all points on the initial design tunnel model is calculated as the point cloud over-excavation value. Furthermore, when the projection point is outside the initial design tunnel model, the minimum distance is the over-excavation value of the tunnel point cloud, that is, the point cloud over-excavation value is positive; when the projection point is inside the initial design tunnel model, the minimum distance is the under-excavation value of the tunnel point cloud, that is, the point cloud over-excavation value is negative.

[0035] Step S202, dividing the tunnel point cloud into regions based on a preset division method, and calculating the average over-excavation and under-excavation value of each point cloud region.

[0036] The preset division method includes a preset division direction and a preset division distance. The preset division direction is determined according to the research direction of the misalignment, and the tunnel point cloud is divided into regions according to the preset division distance to obtain each point cloud region, and the average value of the point cloud over-under-excavation value of each tunnel point cloud in the point cloud region is calculated to obtain the average value of the regional over-under-excavation value.

[0037] In one implementation, the tunnel point cloud is divided into a plurality of point cloud regions in sequence according to the tunnel excavation direction and a preset division distance, and the plurality of point cloud regions are numbered sequentially, and an average value of over-excavation and under-excavation of the regions is calculated.

[0038] In another implementation, the tunnel point cloud is divided into multiple point cloud areas according to the tunnel arc direction and the preset division distance, and the multiple point cloud areas are numbered sequentially to calculate the average value of the regional over-excavation and under-excavation.

[0039] Step S203, performing curve fitting on the average value of regional overbreak and underbreak to obtain an overbreak and underbreak fitting curve.

[0040] According to the curve fitting method, curve fitting is performed in the order of point cloud areas to obtain the over-excavation and under-excavation fitting curve.

[0041] Among them, curve fitting methods include polynomial fitting, moving average method and so on.

[0042] In one implementation, when the point cloud area is divided according to the tunnel excavation direction and the preset division distance, a curve fitting is performed on the average value of regional over- and under-excavation according to the regional number in the tunnel excavation direction to obtain an over- and under-excavation fitting curve, wherein the horizontal coordinate of the over- and under-excavation fitting curve is the regional number, and the vertical coordinate is the corresponding regional over- and under-excavation average value.

[0043] In another implementation method, when the point cloud area is divided according to the tunnel arc direction and the preset division distance, the average value of the regional over- and under-break is fitted with a curve according to the regional number in the tunnel arc direction to obtain the over- and under-break fitting curve. The horizontal axis of the over- and under-break fitting curve is the regional number, and the vertical axis is the corresponding regional over- and under-break average value.

[0044] Step S204, determining the misalignment between tunnel blasting cycles based on the peaks and troughs of the overbreak and underbreak fitting curve.

[0045] Specifically, the peaks and troughs in the over- and under-break fitting curve are identified, the regional over- and under-break average values ​​of adjacent peaks and troughs are compared, the misalignment between tunnel blasting cycles is determined, and the difference between the regional over- and under-break average values ​​between the peaks and troughs is used as the misalignment height.

[0046] In one implementation, when the difference between the average values ​​of regional overbreak and underbreak between adjacent wave crests and wave troughs is greater than a preset value, it is determined that there is a misalignment between adjacent wave crests and wave troughs, and the difference is further used as the misalignment height.

[0047] In another implementation, the identified peaks and troughs are screened, the peaks and troughs with smaller fluctuations are deleted, and the misalignment is further determined based on the difference in average values ​​of regional over-break and under-break between adjacent peaks and troughs after the screening.

[0048] The tunnel blasting misalignment detection method provided in this embodiment uses a three-dimensional point cloud detection method to collect tunnel point clouds and divide the overall tunnel point clouds. The regional over- and under-break average value is used to characterize the tunnel blasting conditions in the region, and the regional over- and under-break average value is representative of the region. Furthermore, a continuous over- and under-break fitting curve is obtained by fitting, which can convert discrete point data into continuous data, and can characterize the tunnel blasting conditions in the overall continuous area, so as to achieve a comprehensive detection of the tunnel. The existing misalignment is determined based on the relationship between the peaks and troughs of the over- and under-break fitting curve. The intelligent detection method has high detection efficiency and reliable detection results.

[0049] In this embodiment, a method for detecting a tunnel blasting misalignment is provided. Figure 5 is a flow chart of another method for detecting misalignment of tunnel blasting according to an embodiment of the present invention. It should be noted that if there are substantially the same results, this embodiment is not limited to Figure 5 The process sequence shown is limited. Figure 5 As shown, the process includes the following steps: Step S501, after the tunnel is excavated by blasting, the tunnel point cloud is collected, and the over-excavation and under-excavation values ​​of the tunnel point cloud are calculated.

[0050] For details, please see Figure 2 Step S201 of the illustrated embodiment will not be described in detail here.

[0051] Step S502, dividing the tunnel point cloud into regions based on a preset division method, and calculating the average over-excavation and under-excavation value of each point cloud region.

[0052] In this implementation, the tunnel point cloud is divided into multiple point cloud areas in sequence according to the tunnel excavation direction and the preset division distance, and the multiple point cloud areas are numbered sequentially, and the average value of over-excavation and under-excavation of the areas is calculated.

[0053] Specifically, the above step S502 includes: Step S5021, determining a tunnel strip at a preset position on the cross-sectional profile of the tunnel, sampling the tunnel point cloud, and obtaining a corresponding point cloud strip.

[0054] The preset area is an area of ​​preset width. Customize the preset area according to the blasting detection requirements.

[0055] One or more point cloud intervals are determined on the cross-sectional point cloud of the irregular tunnel section obtained by actual excavation, and one or more corresponding tunnel strips are determined on the tunnel cross-sectional contour along the tunnel excavation direction according to the point cloud intervals. The point cloud intervals may be uniformly distributed intervals or non-uniformly distributed intervals, that is, the obtained tunnel strips may be uniformly distributed strips or non-uniformly distributed strips.

[0056] Specifically, see Figure 6 , Figure 6 FIG. 1 is a schematic diagram of a tunnel strip according to an embodiment of the present invention. Figure 6 As shown, in one implementation, 8 tunnel strips are obtained based on multiple point cloud intervals.

[0057] Step S5022, dividing the tunnel point cloud on the point cloud strip into regions based on a preset division method, and calculating the average over-excavation and under-excavation value of each point cloud region.

[0058] In one implementation, the tunnel point cloud on the point cloud strip is divided into regions according to the stake number as an interval.

[0059] Specifically, for a point cloud strip, each preset distance is a pile number, and multiple point cloud areas corresponding to multiple pile numbers are divided according to the tunnel excavation direction, wherein each point cloud area includes multiple target pile number point clouds, and the pile numbers of adjacent point cloud areas are adjacent.

[0060] Furthermore, the point cloud over-break and under-break values ​​of all target pile number point clouds in each point cloud area are obtained, and the average value is calculated to obtain the regional over-break and under-break average value corresponding to each point cloud area.

[0061] For example, the preset distance is 5 cm. For a point cloud strip, according to the tunnel excavation direction, a point cloud area is divided every 5 cm for a pile number, and the point cloud areas are named according to the pile numbers. The average over-excavation and under-excavation values ​​of the areas corresponding to the pile numbers are calculated. Figure 7 , Figure 7Schematic diagram of an average value of regional over-excavation and under-excavation according to an embodiment of the present invention. Figure 7 As shown, the horizontal axis is the pile number, and the vertical axis is the average value of regional overbreak and underbreak, and the average value of regional overbreak and underbreak varies along the pile number.

[0062] In this implementation, strip point cloud sampling is performed according to preset positions, and the preset positions are determined according to actual detection requirements, so that the selected tunnel point cloud is representative and the amount of data and calculation is reduced. Regional division is performed on the basis of point cloud strips, and the average value of regional over-excavation and under-excavation can accurately characterize the blasting conditions in each area of ​​the tunnel strip. Among them, dividing point cloud strips according to pile numbers provides a division benchmark, which can plan point cloud strips according to the pile number direction, laying the foundation for the subsequent fitting of over-excavation and under-excavation fitting curves. By calculating the average value of over-excavation and under-excavation of all target pile number point clouds in the point cloud area, the blasting conditions in the pile number area can be accurately characterized, thereby improving the efficiency of subsequent misalignment detection.

[0063] Step S503, performing curve fitting on the average value of regional overbreak and underbreak to obtain an overbreak and underbreak fitting curve.

[0064] For a point cloud strip, curve fitting is performed on the average values ​​of over-break and under-break in continuous regions according to the curve fitting method to obtain the over-break and under-break fitting curve.

[0065] In this implementation, curve fitting is performed according to the pile number sequence, so that the over-break and under-break fitting curve obtained by fitting can accurately characterize the change of the tunnel surface along the pile number sequence.

[0066] For example, see Figure 8 , Figure 8 is a schematic diagram of an over-under excavation fitting curve according to an embodiment of the present invention. Figure 8 As shown, Figure 7 The average value of over-break and under-break in the area shown is fitted by curve according to the continuous direction of pile number, and the result is Figure 8 The continuous curve is the corresponding over-excavation and under-excavation fitting curve.

[0067] Step S504, determining the misalignment between tunnel blasting cycles based on the peaks and troughs of the overbreak and underbreak fitting curve.

[0068] Specifically, the above step S504 includes: Step S5041, determining local peaks and local troughs based on the peaks and troughs of the overbreak and underbreak fitting curve.

[0069] First, the peaks and troughs of the over- and under-break fitting curves are calculated.

[0070] In one implementation, a direct comparison method is used to calculate the peaks and troughs of the over-underbreak fitting curve. and , then the horizontal coordinate is the peak at point x. and , then the horizontal coordinate is the trough at point x.

[0071] In another implementation, the peaks and troughs of the over-undercut fitting curve are calculated by using the first-order and second-order derivative methods. Specifically, on the over-undercut fitting curve, when the first-order derivative of the horizontal coordinate at point x is equal to zero and the second-order derivative is less than zero, the horizontal coordinate at point x is a peak, and when the first-order derivative of the horizontal coordinate at point x is equal to zero and the second-order derivative is greater than zero, the horizontal coordinate at point x is a trough.

[0072] Furthermore, the areas with smaller continuous fluctuations on the overbreak and underbreak fitting curve are determined as local peaks and local troughs.

[0073] Specifically, the peak pile number distance between adjacent peaks is obtained, and when the peak pile number distance is less than a first pile number threshold, it is determined that the adjacent peaks are local peaks; the trough pile number distance between adjacent troughs is obtained, and when the trough pile number distance is less than a second pile number threshold, it is determined that the adjacent troughs are local troughs. In one implementation, the first pile number threshold is equal to the second pile number threshold.

[0074] For example, see Figure 8 , near the pile number 1175, there are multiple continuous and close peaks and troughs, which means there are local peaks and local troughs.

[0075] Step S5042, delete the trough within the local peak and the peak with the smallest average value of regional over-underbreak, delete the peak within the local trough and the trough with the largest average value of regional over-underbreak, and obtain updated peaks and updated troughs.

[0076] Specifically, if there are two peaks and a trough in the local peak area, the trough is deleted, and the peak with the smallest average value of regional over-under-excavation among the two peaks is determined on the over-under-excavation fitting curve, and the peak is deleted. Similarly, if there are two troughs and a peak in the local trough area, the peak is deleted, and the trough with the largest average value of regional over-under-excavation among the two troughs is determined on the over-under-excavation fitting curve, and the trough is deleted.

[0077] After deleting some peaks and troughs, updated peaks and updated troughs are obtained.

[0078] Step S5043, determining the misalignment between hole blasting cycles between adjacent update crests and update troughs, and calculating the difference between the average values ​​of regional over-break and under-break values ​​of adjacent update crests and update troughs to obtain the misalignment height.

[0079] The update peaks and update troughs are peaks and troughs with prominent features, and the offset between adjacent update peaks and update troughs is determined. For example, see Figure 8, between pile numbers 1165 and 1170, there are update peaks and update troughs, there is a misalignment between the pile number of the update peak and the pile number of the update trough, and the height difference of the average over-and-underbreak values ​​in the area corresponding to the pile number is the misalignment difference.

[0080] The tunnel blasting misalignment detection method provided in this embodiment utilizes peaks and troughs to determine the existing misalignment, while considering the influence of smaller peaks and troughs on the misalignment detection, and determines whether there are local peaks and local troughs by pile number distance. The detection method is simple and accurate, and the detection efficiency is improved. Peaks and troughs with smaller continuous fluctuations are deleted, and peaks and troughs with larger distances and larger fluctuations are retained. Peaks and troughs can be effectively screened, thereby improving the accuracy of subsequent misalignment detection.

[0081] In this embodiment, a tunnel blasting misalignment detection device is also provided, which is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0082] This embodiment provides a tunnel blasting misalignment detection device. Fig. 9 is a structural block diagram of a tunnel blasting misalignment detection device according to an embodiment of the present invention. Fig. 9 As shown, the tunnel blasting misalignment detection device comprises: The acquisition module 901 is used to acquire the tunnel point cloud after the tunnel is blasted and excavated, and calculate the over-excavation and under-excavation values ​​of the tunnel point cloud.

[0083] The division module 902 is used to divide the tunnel point cloud into regions based on a preset division method, and calculate the average value of over-excavation and under-excavation of each point cloud region.

[0084] The fitting module 903 is used to perform curve fitting on the average value of regional overbreak and underbreak to obtain an overbreak and underbreak fitting curve.

[0085] The determination module 904 is used to determine the misalignment between tunnel blasting cycles based on the peaks and troughs of the overbreak and underbreak fitting curve.

[0086] In some optional implementations, the partitioning module 902 includes: The sampling unit is used to determine the tunnel strip at a preset position on the cross-sectional profile of the tunnel, sample the tunnel point cloud, and obtain the corresponding point cloud strip.

[0087] The calculation unit is used to divide the tunnel point cloud on the point cloud strip into regions based on a preset division method, and calculate the average value of regional over-excavation and under-excavation of each point cloud region.

[0088] In some optional implementations, the computing unit includes: The pile number division subunit is used to divide the tunnel point cloud on the point cloud strip according to the interval range of the pile number to obtain multiple point cloud areas, each point cloud area includes multiple target pile number point clouds, and the pile numbers of adjacent point cloud areas are adjacent.

[0089] The calculation subunit is used to obtain the point cloud over-break and under-break value of the target pile number point cloud, and calculate the over-break average value of all the target pile number point clouds in the point cloud area to obtain the corresponding area over-break average value.

[0090] In some optional implementations, the fitting module 903 includes: The pile number fitting unit is used to perform curve fitting on the average value of regional overbreak and underbreak in the point cloud area according to the pile number sequence to obtain the overbreak and underbreak fitting curve.

[0091] In some optional implementations, the determining module 904 includes: The first determining unit is used to determine the local peaks and local troughs based on the peaks and troughs of the overbreak and underbreak fitting curve.

[0092] The deletion unit is used to delete the trough within the local peak and the peak with the smallest average value of regional over-underbreak, and delete the peak within the local trough and the trough with the largest average value of regional over-underbreak, so as to obtain updated peaks and updated troughs.

[0093] The second determining unit is used to determine the misalignment between the hole blasting cycles between adjacent update crests and update troughs, and calculate the difference between the average values ​​of the regional over-excavation and under-excavation values ​​of adjacent update crests and update troughs to obtain the misalignment height.

[0094] In some optional implementations, the first determining unit includes: The first acquisition subunit is used to acquire the wave peak pile number distance between adjacent wave peaks, and when the wave peak pile number distance is less than a first pile number threshold, it is determined that the adjacent wave peaks are local wave peaks.

[0095] The second acquisition subunit is used to acquire the trough pile number distance between adjacent troughs, and when the trough pile number distance is less than a second pile number threshold, it is determined that the adjacent troughs are local troughs.

[0096] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0097] The tunnel blasting misalignment detection device in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0098] The embodiment of the present invention also provides a computer device having the above Fig. 9 The tunnel blasting misalignment detection device shown.

[0099] See also Fig.10 , Fig.10 is a schematic diagram of the structure of a computer device provided by an optional embodiment of the present invention, such as Fig.10 As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components are connected to each other using different buses for communication, and can be installed on a common mainboard or installed in other ways as needed. The processor can process instructions executed in the computer device, including instructions stored in or on the memory to display the graphical information of the GUI on an external input / output device (such as a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Fig.10 A processor 10 is taken as an example.

[0100] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0101] The memory 20 stores instructions executable by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiment.

[0102] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0103] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive; the memory 20 may also include a combination of the above types of memory.

[0104] The computer device also includes an input device 30 and an output device 40. The processor 10, the memory 20, the input device 30 and the output device 40 may be connected via a bus or other means. Fig.10 The example of connecting through bus is taken in the following.

[0105] The input device 30 can receive input digital or character information, and generate key signal input related to the user settings and function control of the computer device, such as a touch screen, a keypad, a mouse, a track pad, a touch pad, an indicator bar, one or more mouse buttons, a trackball, a joystick, etc. The output device 40 may include a display device, an auxiliary lighting device (e.g., an LED) and a tactile feedback device (e.g., a vibration motor), etc. The above-mentioned display device includes but is not limited to a liquid crystal display, a light emitting diode, a display and a plasma display. In some optional embodiments, the display device may be a touch screen.

[0106] The embodiment of the present invention also provides a computer-readable storage medium. The method according to the embodiment of the present invention can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0107] A part of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the existence of the computer program instruction in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc., and accordingly, the way in which the computer program instruction is executed by the computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium accessible to the computer.

[0108] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A method for detecting tunnel blasting misalignment, characterized in that: The method comprises: After the tunnel is excavated by blasting, a tunnel point cloud is collected, and a point cloud over-excavation and under-excavation value of the tunnel point cloud is calculated; Divide the tunnel point cloud into regions based on a preset division method, and calculate the average value of regional over-excavation and under-excavation of each point cloud region; Performing curve fitting on the average value of overbreak and underbreak in the area to obtain an overbreak and underbreak fitting curve; The misalignment between tunnel blasting cycles is determined based on the peaks and troughs of the overbreak and underbreak fitting curve.

2. The method for detecting tunnel blasting misalignment according to claim 1, characterized in that: The method of dividing the tunnel point cloud into regions based on a preset division method and calculating the average value of over-excavation and under-excavation in each point cloud region includes: Determine a tunnel strip at a preset position on the cross-sectional profile of the tunnel, and sample the tunnel point cloud to obtain a corresponding point cloud strip; The tunnel point cloud on the point cloud strip is divided into regions based on a preset division method, and the average value of the regional over-excavation and under-excavation of each point cloud region is calculated.

3. The method for detecting tunnel blasting misalignment according to claim 2, characterized in that: The method of dividing the tunnel point cloud on the point cloud strip into regions based on a preset division method and calculating the average value of the regional over-excavation and under-excavation of each point cloud region includes: Dividing the tunnel point cloud on the point cloud strip according to the interval range of the pile number to obtain a plurality of the point cloud regions, each of the point cloud regions including a plurality of point clouds of target pile numbers, and the pile numbers of adjacent point cloud regions are adjacent; The point cloud overbreak and underbreak value of the target pile number point cloud is obtained, and the overbreak and underbreak average values ​​of all the target pile number point clouds in the point cloud area are calculated to obtain the corresponding area overbreak and underbreak average value.

4. The method for detecting tunnel blasting misalignment according to claim 1, characterized in that: The performing curve fitting on the average value of overbreak and underbreak in the region to obtain the overbreak and underbreak fitting curve comprises: A curve fitting is performed on the average overbreak and underbreak values ​​of the point cloud region in the order of pile numbers to obtain the overbreak and underbreak fitting curve.

5. The method for detecting tunnel blasting misalignment according to claim 4, characterized in that: The step of determining the misalignment between tunnel blasting cycles based on the peaks and troughs of the overbreak and underbreak fitting curve comprises: Determining local peaks and local troughs based on the peaks and troughs of the overbreak and underbreak fitting curve; Delete the trough in the local peak and the peak with the smallest average value of regional over-underbreak, and delete the peak in the local trough and the trough with the largest average value of regional over-underbreak, to obtain updated peaks and updated troughs; Determine the misalignment between the hole blasting cycles between adjacent update crests and update troughs, and calculate the difference between the average values ​​of the regional over-excavation and under-excavation values ​​of adjacent update crests and update troughs to obtain the misalignment height.

6. The method for detecting tunnel blasting misalignment according to claim 5, characterized in that: The determining of local peaks and local troughs based on the peaks and troughs of the over-underbreak fitting curve comprises: Acquire the wave peak pile number distance between adjacent wave peaks, and when the wave peak pile number distance is less than a first pile number threshold, determine that the wave peaks between the adjacent wave peaks are the local wave peaks; The trough pile number distance between adjacent troughs is obtained, and when the trough pile number distance is less than a second pile number threshold, it is determined that the troughs between the adjacent troughs are the local troughs.

7. A tunnel blasting misalignment detection device, characterized in that: The device comprises: A collection module, used for collecting tunnel point clouds after tunnel blasting and excavation, and calculating point cloud over-excavation and under-excavation values ​​of the tunnel point clouds; A division module, used for dividing the tunnel point cloud into regions based on a preset division method, and calculating the average value of regional over-excavation and under-excavation of each point cloud region; A fitting module, used for performing curve fitting on the average value of overbreak and underbreak in the area to obtain an overbreak and underbreak fitting curve; The determination module is used to determine the misalignment between tunnel blasting cycles based on the peaks and troughs of the overbreak and underbreak fitting curve.

8. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the tunnel blasting misalignment detection method according to any one of claims 1 to 6 by executing the computer instructions.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the tunnel blasting misalignment detection method according to any one of claims 1 to 6.

10. A computer program product, characterized in that The method comprises computer instructions for causing a computer to execute the tunnel blasting misalignment detection method according to any one of claims 1 to 6.

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